MAGS Project: Multi-agent GeoSimulation and Crowd Simulation

  • Bernard Moulin
  • Walid Chaker
  • Jimmy Perron
  • Patrick Pelletier
  • Jimmy Hogan
  • Edouard Gbei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2825)


Geosimulation aims at modeling systems at the scale of individuals and entity- level units of the built environment and provides a new way to simulate how geographic spaces can be used by their future users, particularly in urban environments. In the MAGS Project we are developing a generic software platform for the creation of Multi-Agent Geo-Simulations involving several thousand agents interacting in virtual geographic environments (in 2D and 3D) and endowed with spatial cognitive capabilities (perception, navigation, reasoning). Our approach is currently applied to the simulation of crowd behaviors in urban environments.


Geographic Information System Multiagent System Mobile Agent Quebec City Crowd Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Beckman, R.J. (ed.): The Dallas – Fort Worth Study., Los Alamos unclassified report LAUR-97-4502LANL, Los Alamos National Laboratory, Los Alamos NM (1997), available at
  2. 2.
    De Floriani, L., Magillo, P.: Visibility algorithms on DTMs. International Journal of Geographic Information Systems 8(1), 13–41 (1994)CrossRefGoogle Scholar
  3. 3.
    Discreet:, (Last visit March 2003) Google Scholar
  4. 4.
    Dodge, M., Doyle, S., Smith, A., Fleetwood, S.: Towards the Virtual City: VR & Internet GIS for Urban Planning, Virtual Reality and Geographical Information Systems Workshop, Birkbeck College (1998)Google Scholar
  5. 5.
    Epstein, S.L., Moulin, B., Chaker, W., Glasgow, J., Gancet, J.: Pragmatism and spatial layout design. In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 189–205. Springer, Heidelberg (2001)Google Scholar
  6. 6.
    Ettema, D., Timmermans, H.: Activity Based Approaches to Travel Analysis, Elsevier Science, Amsterdam (1999)Google Scholar
  7. 7.
    Fotheringham, A.S., O’Kelly, M.E.: Spatial Interaction Models: Formulation and Applications. Kluwer Academic Publishers, Dordrecht (1989)Google Scholar
  8. 8.
    Frank, A.U., Bittner, S., Raubal, M.: Spatial and cognitive simulation with multi-agent systems. In: Montello, D.R. (ed.) COSIT 2001. LNCS, vol. 2205, pp. 124–139. Springer, Heidelberg (2001)Google Scholar
  9. 9.
    Franklin, W.R.: Applications of analytical cartography. Cartography and Geographic Information Systems 27(3), 225–237 (2000)CrossRefGoogle Scholar
  10. 10.
    Gibson, J.: The Ecological Approach to Visual Perception. Houghton Mifflin Company, Boston (1979)Google Scholar
  11. 11.
    Gimblett, H.R.: Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes. Oxford University Press, Oxford (2002)Google Scholar
  12. 12.
    Hacklay, M., O’Sullivan, D., Thurstain-Goldwin, M., Schelhorn, T.: So go downtown: simulating pedestrian movements in town centres. Environment and Planning B: Planning and Design 28(3), 343–359 (2001)CrossRefGoogle Scholar
  13. 13.
    Helbing, D., Farkas, I.J., Vicsek, T.: Simulating dynamic features of escape panic. Nature 407, 487–490 (2000)CrossRefGoogle Scholar
  14. 14.
    Helbing, D., Molnar, P., Schweitzer, F.: Computer simulation of pedestrian dynamics. In: Proceedings of the 3rd International Symposium of SFB 230, Evolution of Natural Structures, Sonderforschungsbereich, Stuttgart, Germany, pp. 229–234 (1999)Google Scholar
  15. 15.
    Helbing, D., Molnar, P., Farkas, I.J., Bolay, K.: Self-organizing pedestrian movements. Environment and Planning B: Planning and Design 28(3), 361–383 (2001)CrossRefGoogle Scholar
  16. 16.
    Intergraph: Geomedia Professional (2003),
  17. 17.
    Jager, W., Popping, R., van de Sande, H.: Clustering and fighting in two-party crowds: simulating approach-avoidance conflict. Journal of Artificial Societies and Social simulationm,  4(3) (2001),
  18. 18.
    Jennings, N., O’Hare, G.: Foundations of Distributed Artificial Intelligence. Wiley, Chichester (1996)Google Scholar
  19. 19.
    Kerridge, J., Hine, J., Wigan, M.: Agent–based modelling of pedestrian movements: the questions that need to be asked. Environment and Planning B: Planning and Design 28(3), 327–341 (2001)CrossRefGoogle Scholar
  20. 20.
    Lee, D.B.: Retrospective on large-scale urban models. Journal of the American Planning Association 60, 35–40 (1994)CrossRefGoogle Scholar
  21. 21.
    Mark, D.M., Freksa, C., Hirtle, S.C., Lloyd, R., Tversky, B.: Cognitive models of geographic space. International Journal of Geographical Information Science 13(8), 747–774 (1999)CrossRefGoogle Scholar
  22. 22.
    Moss, S., Davidsson, P.: Multi-Agent-Based Simulation. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 97–107. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  23. 23.
    Moulin, B., Chaker, W., Gancet, J.: PADI-Simul, an agent-based software which simulates the behaviors of hundreds of actors in a geographic space. Journal on Computers, Environment and Urban Systems (2003) (to appear)Google Scholar
  24. 24.
    O’Sullivan, D., Torrens, P.: Cellular models of urban systems. In: Bandini, S., Worsch, T. (eds.) Theoretical and Practical Issues on Cellular Automata, June 2000, Springer, Heidelberg (2000),; also available from Centre for Advanced Spatial Analysis, Working Paper 22, June 2000Google Scholar
  25. 25.
    Ray, C., Claramunt, C.: Atlas: A distributed system for the simulation of large-scale systems. In: Chen, S.-C., Voisard, A. (eds.) Proceedings of 10th ACM International Symposium On Advances In Geographic Information Systems, McLean, VA, pp. 155–162. ACM Press, New York (2002)Google Scholar
  26. 26.
    Righter, R., Walrand, J.C.: Distributed simulation of discrete event systems. Proceedings of the IEEE 77(1), 99–113 (1989)CrossRefGoogle Scholar
  27. 27.
    Sawyer, R.K.: Artificial societies: multiagent systems and the micro-macro link in sociological theory. Sociological Methods and Research 31(3), 325–363 (2003)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Schelhorn, T., O’Sullivan, D., Haklay, M., Thustain-Goodwin, M.: STREETS: An agentbased pedestrian model, CASA Working Paper 9 (1999),
  29. 29.
    Schillo, M., Fischer, K., Klein, C.T.: The micro-macro link in DAI and sociology. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 133–148. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  30. 30.
    Stam, J.: Interacting with Smoke and Fire in Real Time. Communications of the ACM 43(7), 76–83 (2000)CrossRefGoogle Scholar
  31. 31.
    Torrens, P.M.: Can geocomputation save urban simulation? Throw some agents in the mixture, simmer and wait, CASA Working Paper 32 (2001),
  32. 32.
    Weiss, G. (ed.): Multi-Agent systems. MIT Press, Cambridge (1999)Google Scholar
  33. 33.
    Wolfram, S.: Cellular Automata and Complexity: Collected papers. Addison-Wesley, Reading (1994)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Bernard Moulin
    • 1
  • Walid Chaker
    • 1
  • Jimmy Perron
    • 1
  • Patrick Pelletier
    • 1
  • Jimmy Hogan
    • 1
  • Edouard Gbei
    • 1
  1. 1.Computer Science Department and Center for Research in GeomaticsLaval UniversitySte FoyCanada

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